U.S. patent number 7,221,728 [Application Number 10/625,361] was granted by the patent office on 2007-05-22 for method and apparatus for correcting motion in image reconstruction.
This patent grant is currently assigned to General Electric Company. Invention is credited to Samit Basu, Erdogan Cesmeli, Bruno De Man, Peter Michael Edic, Maria Iatrou.
United States Patent |
7,221,728 |
Edic , et al. |
May 22, 2007 |
Method and apparatus for correcting motion in image
reconstruction
Abstract
One or more techniques are provided for adapting a
reconstruction process to account for the motion of an imaged
object or organ, such as the heart. In particular, projection data
of the moving object or organ is acquired using a slowly rotating
CT gantry. Motion data may be determined from the projection data
or from images reconstructed from the projection data. The motion
data may be used to reconstruct motion-corrected images from the
projection data. The motion-corrected images may be associated to
form motion-corrected volume renderings.
Inventors: |
Edic; Peter Michael (Albany,
NY), Iatrou; Maria (Clifton Park, NY), Cesmeli;
Erdogan (Brookfield, WI), De Man; Bruno (Clifton Park,
NY), Basu; Samit (Niskayuna, NY) |
Assignee: |
General Electric Company
(Niskayuna, NY)
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Family
ID: |
30773031 |
Appl.
No.: |
10/625,361 |
Filed: |
July 23, 2003 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20040136490 A1 |
Jul 15, 2004 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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60397658 |
Jul 23, 2002 |
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60398463 |
Jul 25, 2002 |
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Current U.S.
Class: |
378/8; 378/4 |
Current CPC
Class: |
A61B
6/032 (20130101); A61B 6/4035 (20130101); A61B
6/405 (20130101); A61B 6/4241 (20130101); A61B
6/482 (20130101); A61B 6/541 (20130101); G01N
23/046 (20130101); G06T 5/50 (20130101); G06T
11/005 (20130101); G06T 5/003 (20130101); A61B
6/563 (20130101); G06T 7/246 (20170101); A61B
6/503 (20130101); G06T 2211/408 (20130101); G06T
2211/412 (20130101); Y10S 378/901 (20130101); G06T
2207/10081 (20130101); G06T 2207/30004 (20130101); G01N
2223/419 (20130101); G01N 2223/612 (20130101) |
Current International
Class: |
G01N
23/04 (20060101) |
Field of
Search: |
;378/4-20 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
US. Appl. No. 10/625,719, filed Jul. 23, 2003, Douglas Perry Boyd
et al. cited by other .
Lalush, David C., Feasibility of Transmission Micro-CT with Two
Fan-Beam Sources, IEEE, pp. 1283-1286, Sep. 1-5, 2004, vol. 4, San
Francisco, California. cited by other.
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Primary Examiner: Glick; Edward J.
Assistant Examiner: Song; Hoon
Attorney, Agent or Firm: Fletcher Yoder
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of U.S. Provisional Application
No. 60/397,658 filed on Jul. 23, 2002 and U.S. Provisional
Application 60/398,463 filed on Jul. 25, 2002.
Claims
What is claimed is:
1. A method for reducing motion-related artifacts in a CT image,
comprising: acquiring a projection data set during one or more slow
rotations or a slow partial rotation of a CT gantry about a dynamic
object; determining one or more motion data sets representing the
motion of the dynamic object over time from the projection data set
or from two or more images reconstructed from the projection data
set; reconstructing one or more motion-corrected images of the
dynamic object using the one or more motion data sets, wherein
reconstructing the one or more motion-corrected images comprises
warping a reconstruction grid at a view angle in accordance with
the motion data set for the view angle and backprojecting the
projections corresponding to the view angle onto the warped
reconstruction grid and displaying the one or more motion-corrected
images.
2. The method as recited in claim 1, wherein the dynamic object is
a heart.
3. The method as recited in claim 1, wherein the projection data
set is acquired during one slow rotation of the CT gantry.
4. The method as recited in claim 1, wherein the one or more slow
rotations or the partial rotation take at least ten seconds per
rotation.
5. The method as recited in claim 1, wherein the one or more slow
rotations or the partial rotation take approximately fifteen
seconds per rotation.
6. A method for reducing motion-related artifacts in a CT cardiac
image, comprising: acquiring a projection data set during one or
more slow rotations or a slow partial rotation of a CT gantry about
a heart, wherein the projection data set comprises a plurality of
projections; acquiring a phase data set for the heart from at least
one of an ECG data set, an ultrasound image data set, a tagged MRI
data set, and the projection data set; determining cardiac motion
from the projection data set and the phase data set or from one or
more images generated from the projection data set and the phase
data set; warping one or more reconstruction grids based upon the
determined cardiac motion, wherein each reconstruction grid is
associated with a view angle; backprojecting a corresponding
projection onto a respective warped reconstruction grid for all
view angles to generate a motion corrected image, wherein the
corresponding projection comprises the projection acquired at the
respective view angle associated with the warped reconstruction
grid and displaying the motion corrected image.
7. The method as recited in claim 6, further comprising associating
the motion-corrected images spatially, temporally, or spatially and
temporally.
8. The method as recited in claim 6, wherein the projection data
set is acquired during one slow rotation of the CT gantry.
9. The method as recited in claim 6, wherein the one or more slow
rotations or the partial rotation take at least ten seconds per
rotation.
10. The method as recited in claim 6, wherein the one or more slow
rotations or the partial rotation take approximately fifteen
seconds per rotation.
11. The method as recited in claim 6, wherein the phase data set is
acquired from consistency condition moments of the projection data
set.
12. The method as recited in claim 6, wherein determining cardiac
motion, comprises: reconstructing a phase-specific image for each
phase of interest from a weighted projection set for the phase of
interest, wherein the weighted projection set comprises the
projection data set with projections corresponding to the phase of
interest weighted higher; and determining motion between temporally
adjacent phase-specific images.
13. The method as recited in claim 12, wherein the phase-specific
image is reconstructed iteratively.
14. The method as recited in claim 13, wherein iteratively
reconstructing the phase-specific image uses a non-time resolved
reconstruction to facilitate iterative computation of one or more
temporally varying regions in the phase-specific image.
15. The method as recited in claim 6, wherein determining cardiac
motion, comprises: reconstructing two or more time-resolved images
using the projection data set and the phase data set; and
correlating the location of one or more regions of interest in the
two or more time-resolved images to generate a respective image
displacement map for each pair of time-resolved images.
16. The method as recited in claim 15, further comprising:
determining whether the correlation of the locations of the regions
of interest exceeds a correlation threshold for each image
displacement map; and subdividing the region of interest and
updating the displacement maps until the correlation threshold is
exceeded.
17. The method as recited in claim 6, wherein determining cardiac
motion, comprises: reconstructing two or more phase-specific images
using the projection data set and the phase data set; decomposing
one or more regions of interest in the two or more phase-specific
images to generate wavelet coefficients of the regions of interest;
and analyzing the differences between the wavelet coefficients to
generate a respective image displacement map for each pair of
time-resolved images.
18. The method as recited in claim 17, further comprising:
determining whether the correlation of the wavelet coefficients of
the regions of interest exceeds a correlation threshold for each
image displacement map; and subdividing the region of interest and
updating the displacement maps until the correlation threshold is
exceeded.
19. The method as recited in claim 6, wherein determining cardiac
motion, comprises: reconstructing a time-resolved image at the
phase of minimum motion using the projection data set and the phase
data set; identifying one or more view angles associated with the
next adjacent phase; forward-projecting the time-resolved image at
the identified view angles to generate a set of forward projected
data; minimizing the difference between the forward projected data
and the projection data set to generate a set of phase-specific
displacement data; reconstructing a phase-specific image at the
next phase using the phase-specific displacement data; and
generating a set of phase-specific displacement data for the
phase-specific image at the next phase and for the remaining phases
of interest.
20. The method as recited in claim 6, wherein determining cardiac
motion comprises: identifying one or more view angles corresponding
to a cardiac phase; subtracting the projection data acquired at the
next adjacent views from the projection data acquired at the view
angles to generate one or more respective differential signals for
the cardiac phase; and generating motion data from the one or more
respective differential signals for the remaining phases of
interest.
21. A computer readable media containing a computer program; for
reducing motion-related artifacts in a CT image, comprising: a
routine for acquiring a projection data set during one or more slow
rotations or a slow partial rotation of a CT gantry about a dynamic
object; a routine for determining one or more motion data sets
representing the motion of the dynamic object over time from the
projection data set or from two or more images reconstructed from
the projection data set; and a routine for reconstructing one or
more motion-corrected images of the dynamic object using the one or
more motion data sets, wherein the routine for reconstructing the
one or more motion-corrected images warps a reconstruction grid at
a view angle in accordance with the motion data for the view angle
and backprojects the projections corresponding to the view angle
onto the warped reconstruction grid.
22. The computer program as recited in claim 21, wherein the
dynamic object is a heart.
23. A computer readable media containing a computer program, for
reducing motion-related artifacts in a CT cardiac image,
comprising: a routine for acquiring a projection data set during
one or more slow rotations or a slow partial rotation of a CT
gantry about a heart, wherein the projection data set comprises a
plurality of projections; a routine for acquiring a phase data set
for the heart from at least one of an ECG data set, an ultrasound
image data set, a tagged MRI data set, and the projection data set;
a routine for determining cardiac motion from the projection data
set and the phase data set or from one or more images generated
from the projection data set and the phase data set; a routine for
warping one or more reconstruction grids based upon the determined
cardiac motion, wherein each reconstruction grid is associated with
a view angle; and a routine for backprojecting a corresponding
projection onto a respective warped reconstruction grid for all
view angles to generate a motion corrected image, wherein the
corresponding projection comprises the projection acquired at the
respective view angle associated with the warped reconstruction
grid.
24. The computer program as recited in claim 23, further comprising
a routine for associating the motion-corrected images spatially,
temporally, or spatially and temporally.
25. The computer program as recited in claim 23, wherein the phase
data set is acquired from consistency condition moments of the
projection data set.
26. The computer program as recited in claim 23, wherein the
routine for determining cardiac motion reconstructs a
phase-specific image for each phase of interest from a weighted
projection set, wherein the weighted projection set comprises the
projection data set with projections corresponding to the phase of
interest weighted higher, and determines motion between temporally
adjacent phase-specific images.
27. The computer program as recited in claim 26, wherein the
routine for determining cardiac motion reconstructs the
phase-specific image iteratively.
28. The computer program as recited in claim 27, wherein the
routine for determining cardiac motion uses a non-time resolved
reconstruction to facilitate iterative computation of one or more
temporally varying regions in the phase-specific image.
29. The computer program as recited in claim 23, wherein the
routine for determining cardiac motion reconstructs two or more
time-resolved images using the projection data set and the phase
data set and correlates the location of one or more regions of
interest in the two or more time-resolved images to generate a
respective image displacement map for each pair of time-resolved
images.
30. The computer program as recited in claim 29, wherein the
routine for determining cardiac motion determines whether the
correlation of the locations of the regions of interest exceeds a
correlation threshold for each image displacement map and
subdivides the region of interest and updates the displacement maps
until the correlation threshold is exceeded.
31. The computer program as recited in claim 23, wherein the
routine for determining cardiac motion reconstructs two or more
phase-specific images using the projection data set and the phase
data set, decomposes one or more regions of interest in the two or
more phase-specific images to generate wavelet coefficients of the
regions of interest, and analyzes the differences between the
wavelet coefficients to generate a respective image displacement
map for each pair of time-resolved images.
32. The computer program as recited in claim 31, wherein the
routine for determining cardiac motion determines whether the
correlation of the wavelet coefficients of the regions of interest
exceeds a correlation threshold for each image displacement map,
and subdivides the region of interest and updates the displacement
maps until the correlation threshold is exceeded.
33. The computer program as recited in claim 23, wherein the
routine for determining cardiac motion reconstructs a time-resolved
image at the phase of minimum motion using the projection data set
and the phase data set, identifies one or more view angles
associated with the next adjacent phase, forward-projects the
time-resolved image at the identified view angles to generate a set
of forward projected data, minimizes the difference between the
forward projected data and the projection data set to generate a
set of phase-specific displacement data, reconstructs a
phase-specific image at the next phase using the phase-specific
displacement data; and generates a set of phase-specific
displacement data for the phase-specific image at the next phase
and for the remaining phases of interest.
34. The computer program as recited in claim 23, wherein the
routine for determining cardiac motion identifies one or more view
angles corresponding to a cardiac phase, subtracts the projection
data acquired at the next adjacent views from the respective
projection data acquired at the view angles to generate one or more
respective differential signals for the cardiac phase, and
generates motion data from the one or more respective differential
signals for the remaining phases of interest.
35. A CT image analysis system, comprising: a gantry comprising an
X-ray source configured to emit a stream of radiation wherein the
gantry rotates slowly; a detector configured to detect the stream
of radiation and to generate one or more signals responsive to the
stream of radiation, wherein the detector comprises a plurality of
detector elements; a system controller configured to control the
X-ray source and to acquire a set of projection data during one or
more slow rotations or a partial rotation of the X-ray source about
a dynamic object from one or more of the detector elements via a
data acquisition system; and a computer system configured to
receive the set of projection data, to determine one or more motion
data sets representing the motion of the dynamic object over time
from the set of projection data or from two or more images
reconstructed from the set of projection data, and to reconstruct
one or more motion-corrected images of the dynamic object by
warping a reconstruction grid at a view angle in accordance with
the motion data for the view angle and to backproject the
projections corresponding to the view angles onto the respective
warped reconstruction grids.
36. The CT image analysis system, as recited in claim 35, wherein
the dynamic object is a heart.
37. The CT image analysis system, as recited in claim 35, wherein
the one or more slow rotations or the partial rotation of the
gantry take at least ten seconds per rotation.
38. The CT image analysis system, as recited in claim 35, wherein
the one or more slow rotations or the partial rotation of the
gantry take approximately 15 seconds.
39. A CT image analysis system, comprising: a gantry comprising an
X-ray source configured to emit a stream of radiation, wherein the
gantry rotates slowly; a detector configured to detect the stream
of radiation and to generate one or more signals responsive to the
stream of radiation, wherein the detector comprises a plurality of
detector elements; a system controller configured to control the
X-ray source and to acquire a set of projection data during one or
more slow rotations or a partial rotation of the X-ray source about
a heart from one or more of the detector elements via a data
acquisition system, wherein the set of projection data comprises a
plurality of projections; and a computer system configured to
receive the set of projection data, to acquire a phase data set for
the heart from at least one of an ECG data set, an ultrasound image
data set, a tagged MRI data set, and the projection data set, to
determine cardiac motion from the projection data set and the phase
data set or from one or more images generated from the projection
data set and the phase data set, to warp one or more reconstruction
grids based upon the determined cardiac motion, wherein each
reconstruction grid is associated with a view angle, and to
backproject a corresponding projection onto a respective warped
reconstruction grid for all view angles to generate a motion
corrected image, wherein the corresponding projection comprises the
projection acquired at the respective view angle associated with
the warped reconstruction grid.
40. The CT image analysis system as recited in claim 39, wherein
the computer is further configured to associate the
motion-corrected images spatially, temporally, or spatially and
temporally.
41. The CT image analysis system as recited in claim 39, wherein
the set of projection data is acquired during one slow rotation of
the X-ray source.
42. The CT image analysis system, as recited in claim 39, wherein
the one or more slow rotations or the partial rotation of the
gantry take at least ten seconds per rotation.
43. The CT image analysis system, as recited in claim 39, wherein
the one or more slow rotations or the partial rotation of the
gantry take approximately 15 seconds.
44. The CT image analysis system, as recited in claim 39, wherein
the computer is configured to determine cardiac motion by
reconstructing a phase-specific image for each phase of interest
from a weighted projection set for the phase of interest, wherein
the weighted projection set comprises the projection data set with
projections corresponding to the phase of interest weighted higher
and by determining motion between temporally adjacent
phase-specific images.
45. The CT image analysis system, as recited in claim 42, wherein
the computer is configured to reconstruct the phase-specific images
iteratively.
46. The CT image analysis system, as recited in claim 45, wherein
the computer is further configured to reconstruct the
phase-specific images using a non-time resolved reconstruction to
facilitate iterative computation of one or more temporally varying
regions in the phase-specific image.
47. The CT image analysis system, as recited in claim 39, wherein
the computer is configured to determine cardiac motion by
reconstructing two or more time-resolved images using the
projection data set and the phase data set and by correlating the
location of one or more regions of interest in the two or more
time-resolved images to generate a respective image displacement
map for each pair of time-resolved images.
48. The CT image analysis system, as recited in claim 47, wherein
the computer is further configured to determine whether the
correlation of the locations of the regions of interest exceeds a
correlation threshold for each image displacement map and to
subdivide the region of interest and update the displacement map
until the correlation threshold is exceeded.
49. The CT image analysis system, as recited in claim 39, wherein
the computer is configured to determine cardiac motion by
reconstructing two or more phase-specific images using the
projection data set and the phase data set, and by decomposing one
or more regions of interest in the two or more phase-specific
images to generate wavelet coefficients of the regions of interest,
and by analyzing the differences between the wavelet coefficients
to generate a respective image displacement map for each pair of
time-resolved images.
50. The CT image analysis system, as recited in claim 49, wherein
the computer is further configured to determine whether the
correlation of the wavelet coefficients of the regions of interest
exceeds a correlation threshold for each image displacement map and
to subdivide the region of interest and update the displacement map
until the correlation threshold is exceeded.
51. The CT image analysis system, as recited in claim 39, wherein
the computer is configured to determine cardiac motion by
reconstructing a time-resolved image at the phase of minimum motion
using the projection data set and the phase data set, by
identifying one or more view angles associated with the next
adjacent phase, by forward-projecting the time-resolved image at
the identified view angles to generate a set of forward projected
data, by minimizing the difference between the forward projected
data and the projection data set to generate a set of
phase-specific displacement data, by reconstructing a
phase-specific image at the next phase using the phase-specific
displacement data, and by generating a set of phase-specific
displacement data for the phase-specific image at the next phase
and for the remaining phases of interest.
52. The CT image analysis system, as recited in claim 39, wherein
the computer is configured to determine cardiac motion by
identifying one or more view angles corresponding to a cardiac
phase, by subtracting the projection data acquired at the next
adjacent views from the projection data acquired at the view angles
to generate one or more respective differential signals for the
cardiac phase, and by generating motion data from the one or more
respective differential signals for the remaining phases of
interest.
Description
BACKGROUND OF THE INVENTION
The present invention relates generally to the field of medical
imaging and more specifically to the field of imaging dynamic,
internal tissue, such as cardiac tissue, by computed tomography. In
particular, the present invention relates to the characterization
of internal motion and to the reconstruction of images that account
for the characterized motion.
Computed tomography (CT) imaging systems measure the attenuation of
X-ray beams passed through a patient from numerous angles. Based
upon these measurements, a computer is able to reconstruct images
of the portions of a patient's body responsible for the radiation
attenuation. As will be appreciated by those skilled in the art,
these images are based upon separate examination of a series of
angularly displaced projection images. A CT system processes X-ray
transmission data to generate 2D maps of the line integral of
linear attenuation coefficients of the scanned object at multiple
view angle positions. These data are then reconstructed to produce
images, which are typically displayed on a monitor, and may be
printed or reproduced on film. A virtual 3-D image may also be
produced by a CT examination.
CT scanners operate by projecting fan shaped or cone shaped X-ray
beams from an X-ray source. The X-ray beams may be collimated to
control the shape and spread of the beams. The X-ray beams are
attenuated as they pass through the object to be imaged, such as a
patient. The attenuated beams are detected by a set of detector
elements. Each detector element produces a signal affected by the
attenuation of the X-ray beams, and the data are processed to
produce signals that represent the line integrals of the
attenuation coefficients of the object along the X-ray paths. These
signals are typically called "projection data" or just
"projections". By using reconstruction techniques, such as filtered
backprojection, useful images may be formulated from a collection
of projections. The images may in turn be associated to form a
volume, allowing generation of a volumetric rendering of a region
of interest. The locations of objects, such as pathologies or other
anatomical structures, may then be identified either automatically,
such as by a computer-assisted detection (CAD) algorithm or, more
conventionally, such as by a trained radiologist. CT scanning
provides certain advantages over other types of techniques in
diagnosing disease, particularly because it illustrates the
accurate anatomical information about the body. Further, CT scans
may help physicians distinguish between types of abnormalities more
accurately.
CT imaging techniques, however, may present certain challenges when
imaging dynamic internal tissues, such as the heart. For example,
in cardiac imaging, the motion of the heart causes inconsistencies
in the projection data, which, after reconstruction, may result in
various motion-related image artifacts such as blurring, streaking,
or discontinuities. To reduce the occurrence of motion-related
image artifacts, various techniques may be employed to improve the
temporal resolution of the imaging system, thereby reducing the
effects of the moving tissue. Temporal resolution may generally be
improved by decreasing the rotation time of The CT gantry. In this
way, the amount of motion that occurs within the temporal window
associated with the acquisition of a projection data set is
minimized.
Temporal resolution may be further improved by the choice of
reconstruction algorithm. For example, segment reconstruction
algorithms, such as half-scan reconstruction algorithms, may be
employed in the reconstruction process. The segment reconstruction
algorithms typically reconstruct images using projection data
collected over an angular displacement of the gantry equaling
180.degree. plus the fan angle (.alpha.) of the X-ray beam. Because
the acquisition of projection data during rotation of the gantry by
180.degree.+.alpha. is more rapid than acquisition during
360.degree. of gantry rotation to acquire the requisite projection
data, the temporal resolution of the reconstruction process is
improved.
Multi-sector reconstruction techniques may also improve the
temporal resolution of the reconstructed images by using projection
data acquired during multiple rotations of the gantry using a
multi-slice detector array. The projection data set used for
reconstruction is composed of two or more sectors of projection
data that are acquired from different cardiac cycles on multiple
rotations of the gantry. The sectors comprise the projection data
acquired during a short span of the gantry rotation, typically less
than half of a rotation. The sectors, therefore, have good temporal
resolution if acquired by a rapidly rotating gantry, thereby
providing a good effective temporal resolution for the aggregate
projection data set used in reconstruction.
Using the techniques discussed above, third and fourth generation
CT systems existing today are capable of temporal resolutions of
approximately 300 ms for segment reconstruction strategies.
However, a temporal resolution of approximately 20 ms is desirable
in order to "freeze" cardiac motion, thereby minimizing motion
related artifacts in the reconstructed images. Presently, improving
temporal resolution by the above techniques has typically focused
on further increasing the rotational speed of the gantry.
However, as the rotational speed of the gantry increases, the
centripetal force on the gantry components also increases. The
increasing centripetal force and the tolerances of the gantry
components may comprise, therefore, a mechanical limitation to
increases in gantry velocity. Furthermore, to obtain consistent
image quality in terms of signal-to-noise ratio, a constant X-ray
flux should be delivered to the imaged object or patient during the
scan interval. Achieving a constant X-ray flux, however, places
increased demand on the X-ray tube, particularly in regard to tube
output, and on the components that are required to cool the X-ray
tube. Both mechanical and X-ray flux considerations, therefore, are
obstacles to increasing the gantry rotation speed sufficiently to
achieve a temporal resolution of 20 ms or better in CT
reconstructions. A technique for achieving a temporal resolution
without increasing gantry rotation speed is therefore
desirable.
BRIEF DESCRIPTION OF THE INVENTION
The present technique provides a novel method and apparatus for
improving temporal resolution of a CT imaging system. The technique
employs a slowly rotating CT gantry that acquires projection data
of an object or patient. Motion within the object, such as cardiac
motion within a patient, is identified and used to warp the
reconstruction grid at any instant in time and at any given view
angle of the gantry. The measured projection data may then be
reconstructed, such as by filtered backprojection, on the warped
reconstruction grid to generate a motion corrected image. Motion
corrected images for the entire region of interest may be created
in this manner and associated for viewing, such as by a radiologist
or physician.
BRIEF DESCRIPTION OF THE DRAWINGS
The foregoing and other advantages and features of the invention
will become apparent upon reading the following detailed
description and upon reference to the drawings in which:
FIG. 1 is a diagrammatical view of an exemplary imaging system in
the form of a CT imaging system for use in producing processed
images, in accordance with one aspect of the present technique;
FIG. 2 is another diagrammatical view of a physical implementation
of The CT system of FIG. 1, in accordance with one aspect of the
present technique;
FIG. 3 is a flowchart depicting a technique for generating
motion-corrected images of a moving object, in accordance with one
aspect of the present technique;
FIG. 4 is a flowchart depicting the technique for generating
motion-corrected cardiac images using an exemplary CT system, in
accordance with one aspect of the present technique;
FIG. 5 is a flowchart depicting a technique for determining cardiac
motion, in accordance with one aspect of the present technique;
FIG. 6 is a flowchart depicting a technique for reconstructing
images for use in determining cardiac motion, in accordance with
one aspect of the present technique;
FIG. 7 is a flowchart depicting an additional technique for
determining cardiac motion, in accordance with one aspect of the
present technique;
FIG. 8 is a flowchart depicting another technique for determining
cardiac motion, in accordance with one aspect of the present
technique;
FIG. 9 is a flowchart depicting a further technique for determining
cardiac motion, in accordance with one aspect of the present
technique; and
FIG. 10 is a flowchart depicting an additional technique for
determining cardiac motion, in accordance with one aspect of the
present technique.
DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS
FIG. 1 illustrates diagrammatically an imaging system 10 for
acquiring and processing image data. In the illustrated embodiment,
system 10 is a computed tomography (CT) system designed to acquire
X-ray projection data, to reconstruct the projection data into an
image, and to process the image data for display and analysis in
accordance with the present technique. In the embodiment
illustrated in FIG. 1, imaging system 10 includes a source of X-ray
radiation 12 positioned adjacent to a collimator 14. In this
exemplary embodiment, the source of X-ray radiation source 12 is
typically an X-ray tube.
Collimator 14 permits a stream of radiation 16 to pass into a
region in which a subject, such as a human patient 18 is
positioned. The stream of radiation 16 may be generally fan or cone
shaped, depending on the configuration of the detector array,
discussed below, as well as the desired method of data acquisition.
A portion of the radiation 20 passes through or around the subject
and impacts a detector array, represented generally as reference
numeral 22. Detector elements of the array produce electrical
signals that represent the intensity of the incident X-ray beam.
The signals generated by the detector array 22 may be subsequently
processed to reconstruct an image of the features within the
subject.
A variety of configurations of the detector 22 may be employed in
conjunction with the techniques described herein. For example, the
detector 22 may be a multi-row detector, such as a detector 22
comprising eight or sixteen rows of detector elements, that
achieves limited longitudinal coverage of the object or patient
being scanned. Similarly, the detector 22 may be an area detector,
such as a detector 22 comprising hundreds of rows of detector
elements, that allow positioning of the entire object or organ
being imaged within the field of view of the system 10 at each
angular position of the gantry, enabling measurement of the
two-dimensional projection data required for image reconstruction
of the whole organ. Other detector 22 configurations may also be
suitable. For example, the detector array 22 may comprise a
central, high-resolution portion with or without a lower-resolution
portion extending from two or more sides of the central portion. If
present, the lower resolution extension may expand the field of
view of the system 10 to encompass the entire object being imaged.
In general, it is desirable to center the object or organ to be
imaged, particularly a dynamic organ such as the heart, within the
field of view defined by the detector array 22.
The source 12 is controlled by a system controller 24, which
furnishes both power, and control signals for CT examination
sequences. Moreover, detector 22 is coupled to the system
controller 24, which commands acquisition of the signals generated
in the detector 22. The system controller 24 may also execute
various signal processing and filtration functions, such as for
initial adjustment of dynamic ranges, interleaving of digital image
data, and so forth. In general, system controller 24 commands
operation of the imaging system to execute examination protocols
and to process acquired data. In the present context, system
controller 24 also includes signal processing circuitry, typically
based upon a general purpose or application-specific digital
computer, associated memory circuitry for storing programs and
routines executed by the computer, as well as configuration
parameters and image data, interface circuits, and so forth.
In the embodiment illustrated in FIG. 1, system controller 24 is
coupled to a linear positioning subsystem 26 and rotational
subsystem 28. The rotational subsystem 28 enables the X-ray source
12, collimator 14 and the detector 22 to be rotated one or multiple
turns around the patient 18. It should be noted that the rotational
subsystem 28 might include a gantry. Thus, the system controller 24
may be utilized to operate the gantry. The linear positioning
subsystem 26 enables the patient 18, or more specifically a patient
table, to be displaced linearly. Thus, the patient table may be
linearly moved within the gantry to generate images of particular
areas of the patient 18. Although the depicted system 10 is a third
generation CT scanner, the methods to generate signals
representative of cardiac motion described herein apply to all
advanced generation CT systems.
Additionally, as will be appreciated by those skilled in the art,
the source of radiation may be controlled by an X-ray controller 30
disposed within the system controller 24. Particularly, the X-ray
controller 30 is configured to provide power and timing signals to
the X-ray source 12. A motor controller 32 may be utilized to
control the movement of the rotational subsystem 28 and the linear
positioning subsystem 26.
Further, the system controller 24 is also illustrated comprising a
data acquisition system 34. In this exemplary embodiment, the
detector 22 is coupled to the system controller 24, and more
particularly to the data acquisition system 34. The data
acquisition system 34 receives data collected by readout
electronics of the detector 22. The data acquisition system 34
typically receives sampled analog signals from the detector 22 and
converts the data to digital signals for subsequent processing by a
computer 36.
The computer 36 is typically coupled to the system controller 24.
The data collected by the data acquisition system 34 may be
transmitted to the computer 36 for subsequent processing and
reconstruction. The computer 36 may comprise or communicate with a
memory 38 that can store data processed by the computer 36 or data
to be processed by the computer 36. It should be understood that
any type of computer accessible memory device capable of storing
the desired amount of data and/or code may be utilized by such an
exemplary system 10. Moreover, the memory 38 may comprise one or
more memory devices, such as magnetic or optical devices, of
similar or different types, which may be local and/or remote to the
system 10. The memory 38 may store data, processing parameters,
and/or computer programs comprising one or more routines for
performing the processes described herein.
The computer 36 may also be adapted to control features enabled by
the system controller 24, i.e., scanning operations and data
acquisition. Furthermore, the computer 36 may be configured to
receive commands and scanning parameters from an operator via an
operator workstation 40 typically equipped with a keyboard and
other input devices (not shown). An operator may thereby control
the system 10 via the input devices. Thus, the operator may observe
the reconstructed image and other data relevant to the system from
computer 36, initiate imaging, and so forth.
A display 42 coupled to the operator workstation 40 may be utilized
to observe the reconstructed image. Additionally, the reconstructed
image may also be printed by a printer 44, which may be coupled to
the operator workstation 40. The display 42 and printer 44 may also
be connected to the computer 36, either directly or via the
operator workstation 40. Further, the operator workstation 40 may
also be coupled to a picture archiving and communications system
(PACS) 46. It should be noted that PACS 46 might be coupled to a
remote client 48, radiology department information system (RIS),
hospital information system (HIS) or to an internal or external
network, so that others at different locations may gain access to
the image data.
It should be further noted that the computer 36 and operator
workstation 40 may be coupled to other output devices, which may
include standard, or special purpose computer monitors and
associated processing circuitry. One or more operator workstations
40 may be further linked in the system for outputting system
parameters, requesting examinations, viewing images, and so forth.
In general, displays, printers, workstations, and similar devices
supplied within the system may be local to the data acquisition
components, or may be remote from these components, such as
elsewhere within an institution or hospital, or in an entirely
different location, linked to the image acquisition system via one
or more configurable networks, such as the Internet, virtual
private networks, and so forth.
Referring generally to FIG. 2, an exemplary imaging system utilized
in a present embodiment may be a CT scanning system 50. The CT
scanning system 50 may be a multi-slice detector CT (MDCT) system
that offers selection of axial coverage, while providing high
gantry rotational speed and high spatial resolution. Alternately,
The CT scanning system 50 may be a volumetric CT (VCT) system
utilizing a cone-beam geometry and an area detector to allow the
imaging of a volume, such as an entire internal organ of a patient.
Furthermore, as noted above, The CT scanning system 50 may be a
third generation CT imaging system, as depicted, or may be an
advanced generation CT imaging system.
The CT scanning system 50 is illustrated with a frame 52 and a
gantry 54 that has an aperture 56 through which a patient 18 may be
moved. A patient table 58 may be positioned in the aperture 56 of
the frame 52 and the gantry 54 to facilitate movement of the
patient 18, typically via linear displacement of the table 58 by
the linear positioning subsystem 26 (see FIG. 1). The gantry 54 is
illustrated with the source of radiation 12, typically an X-ray
tube that emits X-ray radiation from a focal point 62. For cardiac
imaging, the stream of radiation is directed towards a cross
section of the patient 18 including the heart.
In typical operation, X-ray source 12 projects an X-ray beam from
the focal point 62 toward the detector array 22. The collimator 14
(see FIG. 1), such as lead or tungsten shutters, typically defines
the size and shape of the X-ray beam that emerges from the X-ray
source 12. The detector 22 is generally formed by a plurality of
detector elements, which detect the X-rays that pass through and
around a subject of interest, such as the heart or chest. Each
detector element produces an electrical signal that represents the
intensity of the X-ray beam at the position of the element during
the time the beam strikes the detector. The gantry 54 is rotated
around the subject of interest so that a plurality of radiographic
views may be collected by the computer 36.
Thus, as the X-ray source 12 and the detector 22 rotate, the
detector 22 collects data of the attenuated X-ray beams. Data
collected from the detector 22 then undergoes pre-processing and
calibration to condition the data to represent the line integrals
of the attenuation coefficients of the scanned objects. The
processed data, commonly called projections, may then be filtered
and backprojected to formulate an image of the scanned area. A
formulated image may incorporate, in certain modes, projection data
acquired from less or more than 360 degrees of gantry rotation.
Once reconstructed, the cardiac image produced by the system of
FIGS. 1 and 2 reveals the heart of the patient 18. As illustrated
generally in FIG. 2, the image 64 may be displayed to show patient
features, such as indicated at reference numeral 66 in FIG. 2. In
traditional approaches to diagnosis of medical conditions, such as
disease states, and more generally of medical conditions or events,
a radiologist or physician would consider the reconstructed image
64 to discern characteristic features of interest. Such features 66
include coronary arteries or stenotic lesions of interest, and
other features, which would be discernable in the image, based upon
the skill and knowledge of the individual practitioner. Other
analyses may be based upon capabilities of various CAD
algorithms.
Reconstruction of Motion-Corrected Images
As will be appreciated by those skilled in the art, reconstruction
of an image 64 may be complicated by a variety of factors. For
example, reconstructed images 64 of dynamic tissue may include
motion-related image artifacts that are attributable to the
movement of the tissue during imaging. To reduce motion-related
artifacts, it is generally desirable to improve the temporal
resolution of The CT reconstruction process.
For example, referring to FIG. 3, a process for improving the
effective temporal resolution of a CT reconstruction process is
depicted. As depicted as step 80, radiographs of the object within
the field of view are acquired by a slowly rotating gantry 54, such
as a gantry rotation that takes 10 or more seconds to complete. In
one aspect of the present technique the gantry 54 completes a
rotation in approximately fifteen seconds. The radiographs may be
acquired in a single rotation of the gantry 54 or over the course
of more than one such rotation. Alternatively, the radiographs may
be acquired over the course of a partial rotation, i.e., less than
360.degree. of rotation, depending on the reconstruction
methodology to be employed. If the object being imaged undergoes
repetitive or cyclic motion, more than one cycle of motion may be
completed during the rotation or rotations of the gantry 54. For
example, if the object being imaged is a heart, more than one
cardiac cycle will typically be completed during a single rotation
of the gantry 54. For simplicity, a single rotation of the slowly
rotating gantry 54 will be assumed, though one skilled in the art
will readily understand that the techniques described may be easily
adapted to process projection data 82 collected by multiple slow
rotations of the gantry 54 for additional locations on the object
being imaged.
The acquired radiographs may be processed to form a projection data
set 82. The motion of the imaged object may be determined at
discernible phases, as depicted at step 84, to form a set of motion
data 86. The motion at a phase of movement may be determined from
the projection data set 82 and/or from one or more images 88
reconstructed from the projection data set 82. The determination of
motion data 86 may be facilitated by identifying specific phases of
motion of the object being imaged using the projection data 82
themselves or from an external indicator of the phase information
of the imaged object, for instance with a measured
electrocardiogram (ECG) signal if the object is the heart. Once
determined, the motion data 86 may be used to correct for the
motion of the object during imaging when reconstructing the
projection data set 82, as depicted at step 90. One or more
motion-corrected images 92 may be generated by the motion-corrected
reconstruction process.
Application of the general technique depicted in FIG. 3 to cardiac
imaging using a CT scanning system 50 is depicted in FIG. 4. A
projection data set 82 is a collection of processed radiographs
acquired by a slowly rotating CT gantry 54, as depicted at step 80.
The projection data 82 may contain data inconsistencies
attributable to cardiac motion during the data acquisition step
80.
In addition, phase data 96 of the cardiac cycle may be acquired or
generated. The phase data 96 may be derived from the projection
data 82, from an ECG signal acquired concurrent with the
radiographs, as depicted at step 98, or from imaging data acquired
via other imaging modalities, as depicted at step 100. The phase
data 96 may facilitate the identification of the motion of the
heart at a phase of the cardiac cycle, as depicted at step 102. For
example, the phase data 96 may facilitate the estimation of motion
during a specific phase of the cardiac cycle from either the
projection data 82 or from phase-specific images 104 reconstructed
from the projection data 82, such as may be generated via
retrospective gating of the projection data 82 using the phase data
96. Once the motion during the entire cardiac cycle is identified,
the reconstruction grid at a specified view angle associated with a
set of projection data may be warped or adapted to account for the
motion of the heart at that phase of the cardiac cycle, thereby
using the phase data 96, as depicted at step 106. The resulting
warped reconstruction grid 108 mitigates the inconsistencies in the
projection data 82 attributable to the cardiac motion at the
particular view angle. If additional projection data are to be
filtered and backprojected, as determined at decision block 110,
the motion identification, the usage of the phase data 96, and the
subsequent acts may be repeated for the remaining view angles of
interest, as depicted at step 112.
After a warped reconstruction grid 108 has been generated, the
projection data set 82 may then be filtered and backprojected onto
the respective warped reconstruction grid 108 for each view angle
position. After projection data 82 from all gantry view angles have
been filtered and back-projected on appropriate warped
reconstruction grids 108 relative to phase data 96,
motion-corrected cardiac images 116 are generated, as depicted at
step 114. As one skilled in the art will understand, the order of
these steps may vary. For example, the motion-corrected cardiac
images 116 may be reconstructed as each respective warped
reconstruction grid 108 is generated, as depicted in FIG. 4.
Alternately, reconstruction of the motion-corrected images 116 may
occur after the generation of all the warped reconstruction grids
108 of interest as a separate and/or discrete process. Such
variations in the execution of the process are considered to be
well within the scope of the technique.
Once the desired motion-corrected cardiac images 116 have been
reconstructed, the images may be associated spatially and/or
temporally. For example, spatially proximate or adjacent images may
be associated spatially, as depicted at step 118, to generate a
static volume rendering 120 at a point in time during the cardiac
cycle or at a desired phase. Similarly, temporally proximate or
adjacent images 116 may be associated temporally, as depicted at
step 122, to generate an image sequence or video 124 depicting a
slice or cross-section over time, i.e., over the course of the
cardiac cycle. Similarly, the motion-corrected cardiac images 116
may be associated both spatially and temporally to generate a
dynamic volume rendering 126 depicting the motion of the volume
over time.
Determination of Motion
As will be readily apprehended by those skilled in the art, the
motion of the heart may be determined in various ways that may be
used in conjunction with the process for generating
motion-corrected cardiac images 116 described above. For example,
motion may be identified using only the projection data 82. In
particular, because the projection data 82 varies only slightly
from view to view, the motion information may be determined at step
102 by identifying the warping of the image space to account for
the inconsistencies observed in the projection data 82.
Image data acquired, either concurrently or sequentially, by other
imaging modalities, such as cardiac ultrasound or tagged MRI, may
be used to determine the cardiac motion directly at step 102.
Alternately, image data from other modalities, ECG data, or data
derived from the projections 82 themselves, such as via techniques
employing consistency conditions to analyze the projection data 82
and/or to compare the moments of the projection data 82, may be
used to determine phase data 96, i.e., the timing associated with
the respective cardiac phases during the acquisition of the
projection data 82. The phase data 96 may be used to
retrospectively gate, i.e., select, the projection data 82 that
corresponds in phase. The gated projection data may be
reconstructed to generate images of the heart at the various phases
of the cardiac cycle. The phase-specific images may then be used to
determine the motion of the heart from phase to phase at step 102.
While these generalized techniques are acceptable for providing
motion information that may be used to form a warped reconstruction
grid 108, other techniques also exist for determining the cardiac
motion at step 102.
A. Iterative Reconstruction Using Weighted Views
For example, referring to FIG. 5, one technique for generating
reconstructions to aid in determining cardiac motion is described
in detail. A filtered backprojection of the complete, projection
data set 82 acquired during 360.degree. of gantry rotation is
performed to reconstruct a non-time resolved image 140, as depicted
at step 142. The inconsistencies in the projection data set 82
attributable to cardiac motion result in motion-related artifacts,
such as streaking or blurring, in the non-time resolved image
140.
The phase data 96 may be used to identify sets of projections in
the projection data set 82 which were acquired at the same cardiac
phase, as depicted at step 144. The identified projections 146
incorporate the desired phase data with the projections comprising
the projection data set 82 and may be used to reconstruct
phase-specific images 104, as depicted at step 150 and shown in
detail in FIG. 6. In particular, a weighted projection data set 154
is created using the full projection data set 82 by weighting the
identified projections 146 associated with the cardiac phase of
interest as more important, as depicted at step 156. At the expense
of temporal resolution, adjacent views to the views of the phase of
interest may be weighted, to an equal or lesser extent, to further
reduce image artifacts resulting from statistical noise in the
measurements.
Using iterative reconstruction techniques known to those of
ordinary skill in the art, the weighted data set 154 may be
iteratively reconstructed to update the region of interest, as
shown in step 162. The non-time resolved image 140 may be used as a
reference or initial image, depending on the iterative methodology
employed. The iterative process may continue until the image
quality and the temporal definition are determined to be
acceptable, at which time it may be considered a phase-specific
image 164. The phase-specific image 164 preserves the phase
information introduced by the weighting process but also
incorporates projections from other phases to contribute to the
overall image, thereby improving the overall quality of the image.
If desired, only the region of interest corresponding to the heart
may be iteratively updated. Feathering may be employed to prevent
discontinuities in the phase-specific image 164 between the
iteratively updated region of interest and the remainder of the
image.
A determination may be made whether phase-specific images 164 exist
for all of the phases of interest at decision block 166. If
phase-specific images do not exist for all of the phases of
interest, the next phase is proceeded to at step 168 and a weighted
projection data set 154 is generated for the next phase of
interest. Once a phase-specific image 164 exists for all phases of
interest, the phase-specific images 104 may be used to identify
motion between temporally adjacent images, as depicted at step 170
of FIG. 5. For example, the motion identified between the
temporally adjacent, phase-specific images 104 may be used to warp
the reconstruction grid at all view angles, as depicted at step
106. The image grid warping for each view angle position
corresponds to the phases of the cardiac cycle that the projection
data were acquired.
B. Correlation-Based Estimation
One technique for determining motion is an image-based correlation
approach. This approach uses the phase data 96 and the projection
data 82 to reconstruct phase-specific images 104, as depicted in
step 182 of FIG. 7. The phase-specific images 104 may be formed
using iterative reconstruction of weighted views, as discussed
above with regard to step 162 of FIG. 6. One or more regions of
interest in phase-specific images 104 are correlated to respective
regions in one or more temporally neighboring phase-specific images
104 to determine the probable motion of the regions of interest
over time, as depicted at step 184. An image displacement map 186
may be generated using the probable motion data of the regions of
interest generated by the correlation process. In this manner, a
displacement map 186 may be generated for each image of the heart
over time. The displacement and time information may be combined to
form a velocity map for each adjacent pair of phase-specific images
104 if desired. Once velocity and/or displacement maps 186 are
generated for each phase of interest, as determined at decision
block 188, the motion information may be used to warp the
reconstruction grids at the respective view angles, as depicted at
step 106. If maps 186 have not been generated for each phase of
interest, as determined at decision block 188, the next phase is
processed, as depicted at step 190, until all phases of interest
have been processed.
As depicted in FIG. 7, a multi-resolution aspect may be
incorporated into the correlation-based approach. The
multi-resolution aspect may be useful where the regions of interest
exhibit complex or multiple directions of motion. In particular,
after determination of the motion of the regions of interest, as
identified in the velocity and/or displacement map 186, a
determination is made at decision block 192 as to whether the
temporally adjacent regions of interest are correlated to the
desired degree, i.e., if the desired correlation threshold is met
or exceeded. For example, a correlation threshold of 95% may be
implemented.
If the correlation threshold is met, processing proceeds as
described above with any remaining phases being processed and the
motion information used to warp the respective reconstruction grids
108. If, however, the correlation threshold is not met or exceeded,
the region or regions of interest may be subdivided, as depicted at
step 194, and the correlation process repeated until the
correlation threshold is met by the subdivided regions of interest.
In this manner the complex motion of the heart, or other object,
may be determined and accurately used to warp the reconstruction
grids at step 106.
C. Wavelet Decomposition
Similarly, wavelet decomposition may be used in the motion
determination step 102. This approach uses the phase data 96 and
the set of projection data 82 to reconstruct phase-specific images
104 as depicted at step 202 in FIG. 8. The phase-specific images
104 may be formed using iterative reconstruction of weighted views,
as discussed above with regard to step 162 of FIG. 6. One or more
regions of interest in the phase-specific images 104 are decomposed
via a wavelet function, as depicted at step 204, to generate
wavelet coefficients 206 for the regions of interest at the phase
of interest. In particular, using the relevant wavelet basis
functions, the local frequency information of the regions of
interest is better captured relative to approaches using
Fourier-based analysis performed on the entire image. The
differences between the wavelet coefficients associated with the
regions of interest may be analyzed for regions in temporally
adjacent reconstructions to generate an image displacement map 208
and/or velocity map describing the local motion of the regions of
interest, as depicted at step 210. Once the velocity and/or
displacement maps 208 of each phase of interest are generated, as
determined at decision block 212, the motion information
incorporated in the maps may be used to warp the reconstruction
grids at the respective view angles, as depicted at step 106. If
maps 208 have not been generated for each phase of interest, the
next phase is processed, as depicted at step 214, until all phases
of interest have been processed.
As with the correlation-based approach depicted in FIG. 7, a
multi-resolution aspect may be incorporated into the wavelet
decomposition approach to accommodate complex motion within the
regions of interest. In particular, after determination of the
motion of the regions of interest from the velocity and/or
displacement map 186, a determination may be made at decision block
216 as to whether all of the temporally adjacent regions of
interest are correlated to the desired degree, as discussed above
with regard to the correlation-based approach.
If the correlation threshold is met, processing proceeds as
described above with any remaining phases being processed and the
motion information used to warp the respective reconstruction
grids. If, however, the correlation threshold is not met or
exceeded, the region or regions of interest may be subdivided, as
depicted at step 218, and the decomposition and analysis processes
repeated until the correlation threshold is met by the subdivided
regions of interest. In this manner the complex motion of the
heart, or other object, may be determined and accurately used to
warp the reconstruction grids at step 106.
D. Sparse, Differential-Projection Image Grid Motion
Determination
The motion determination step 102 may also be accomplished by using
the projection data 82 and the phase data 96 to reconstruct a
time-resolved image at the phase of minimum motion 230, as depicted
at step 232 of FIG. 9. Although not discussed, in other
embodiments, it is possible to generate the initial images using
other techniques. For example, since the non-time resolved image
140 comprises components of the time-resolved images, it may be
used with alternate processing steps than described herein to
accomplish the same task. Moreover, the initial image can be
generated by a variety of reconstruction approaches, for example
with filtered back-projection or iterative reconstruction
techniques.
The view angles of temporally adjacent phases may be identified
using the phase data 96, as depicted at step 234. The time-resolved
image 230 may be forward-projected for the identified view angles
to generate forward-projected data 236, as depicted at step 238.
Phase-specific displacement data 240 may be generated by
optimizing, generally by minimizing, the difference between the
forward-projected data 236 and the measured projection data 82 at
the temporally adjacent phase, as depicted at step 242. For
example, minimizing the difference may be accomplished by
generating a map of motion estimation that appropriately warps the
reconstruction grid during the increment in phase, thereby
improving the similarity of the measured data with the
forward-projected data. As one might expect, the motion estimates
are considered accurate when little or no error exists between the
difference of the measured projection data 82 of a temporally
adjacent phase, as determined at block 234, and the
forward-projected data 236 of the reconstruction volume after
applying the phase-specific displacement data 240 to the
reconstruction grid.
The optimization and/or minimization process at step 242 may be
accomplished by a variety of approaches. For example, the image
motion may be linearized and solved iteratively. Alternatively, the
problem may be expressed in terms of the optic flow equation,
allowing the solution to be determined by the solution of a large
set of linear equations. The process may also be accomplished by
subtracting the forward-projected data 236 from the measured
projection data 82 identified in a temporally adjacent phase at
step 234. The differential projection data thereby obtained may be
backprojected to generate an image of the temporal derivative of
the object motion in the image. The temporal derivative data may
then be used to generate a gradient of the original time-resolved
image 230 while applying the constraint conditions for optic flow
to estimate object motion occurring between reconstructed images of
adjacent phases of interest.
The phase-specific displacement data 240 thereby obtained provides
a three-dimensional estimate of motion for the time-resolved image
230 and therefore allows the generation of an image 244 at the next
temporal phase, as depicted at step 246, by incorporating the image
grid warping of the reconstructed images during the backprojection
process. The process may be repeated until all phases of interest
have been reconstructed, as determined at decision block 248. The
phase-specific displacement data 240 thereby generated may be used
to warp the reconstruction grids at the respective view angles, as
depicted at step 106.
This approach may be modified by parameterizing the motion in the
image using a three-dimensional function or set of
three-dimensional basis functions. As one skilled in the art will
readily understand, the same techniques can be applied to
two-dimensional images as well. The coefficients of the functions
or functions may be estimated from the displacement data 240 to
form the reconstructed image of the next phase 244, as depicted in
block 246. This approach provides a way to reconstruct a quantity
based upon motion distribution as opposed to the linear attenuation
coefficients visualized as intensities. Alternately, both the
motion distribution and the linear attenuation can be reconstructed
simultaneously in a similar fashion.
E. Time-Resolved, Differential-Projection Modeled Motion
Determination
The motion determination step 102 may also be accomplished by using
the projection data 82 and the phase data 96 to identify the view
angles of projection data at the phase of interest, as depicted at
step 260 of FIG. 10. The projection data set from the next adjacent
view is subtracted from the projection data at the phase of
interest at step 262 to generate a differential signal 264. The
differential signal 264 represents the motion of the object between
the two views along a substantially common ray. The motion of the
heart may be estimated from the differential signal 264 in
accordance with the null space, i.e., the motion of the heart can
be estimated orthogonal to, but not along the ray comprising the
differential signal 264. If desired a correction factor may be
introduced to account for the rotation of the object, i.e., the
heart, as represented in the differential signal 264.
If additional views of the phase of interest remain, as determined
at decision block 266, the process proceeds to the next view, as
depicted at step 268, until all views of the phase of interest have
been processed. The motion of the heart within the image may be
determined from the combined differential signals, as depicted at
step 270. The respective reconstruction grids may be warped at the
respective view angles, as depicted at step 106, using the motion
data determined from the combined differential signals 264. If
additional phases of interest remain to be processed, as determined
at step 272, the process proceeds to the next phase, as depicted at
step 274, and continues until motion estimation is generated for
each view of each phases of interest.
As one of ordinary skill in the art will appreciate, the processes
for determining and correcting motion described herein may be
provided as one or more routines executable by the computer 36 or
by other processor-based components of the CT system 10. The
routines may be stored or accessed on one or more computer-readable
media, such as magnetic or optical media, which may be local to the
computer 36 or processor-based component or may be remotely
accessible via a network connection, such as via the Internet or a
local area network. Furthermore, access to or operation of the
routines may be provided to an operator via the operator
workstation 40 as part of the normal operation of a CT imaging
system 10.
While the above techniques are useful in the determination of
cardiac motion for use in reconstructing motion-corrected images
and for improving the temporal resolution of reconstructed images,
other techniques may also be employed and are within the scope of
this disclosure. Likewise, the present techniques for
reconstructing motion-corrected images and for determining motion
may be applied to the imaging of moving objects other than the
heart. Indeed, discussion of cardiac imaging is presented merely to
facilitate explanation of the present techniques. Additionally, use
of the motion estimates in the invention has been discussed in the
context of filtered back-projection reconstruction techniques.
However, the motion estimates may be used with other reconstruction
strategies, such as with iterative reconstruction techniques.
Indeed, while the invention may be susceptible to various
modifications and alternative forms, specific embodiments have been
shown by way of example in the drawings and have been described in
detail herein. However, it should be understood that the invention
is not intended to be limited to the particular forms disclosed.
Rather, the invention is to cover all modifications, equivalents,
and alternatives falling within the spirit and scope of the
invention as defined by the following appended claims.
* * * * *